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Sentiment analysis involves determining the sentiment expressed in a piece of text, classifying it as positive, negative, or neutral. In this project, it helps classify restaurant reviews to better understand customer feedback, providing insights into customer satisfaction and areas for improvement.
This project detects spam messages in SMS, including those written in regional languages typed in English. It uses an extended SMS dataset and applies the Monte Carlo method with various supervised learning algorithms to improve spam detection.
Breast Cancer Data Analysis: Analyzes and classifies breast cancer data using a Naive Bayes classifier with preprocessing, label encoding, and k-fold cross-validation. Cars Dataset Analysis: Explores a cars dataset with data loading, statistics, and visualizations, including price distribution and correlation heatmap. Hayes-Roth Classification: C
Culled from the UCI Machine Learning Repository, the Dry Bean Dataset (licensed under CC BY 4.0) provides valuable insights into bean classification and is a valuable resource for machine learning enthusiasts.
In this study we seek to predict employee attrition with KNN clustering and Naive Bayes, and to predict employee salary using multiple linear regression
The Student Success Predictor employs Naive Bayes to assess the likelihood of students achieving scores above 90, integrating study hours and personal factors. This model aids educators in identifying key elements influencing academic excellence, facilitating targeted interventions for enhanced student success. Contributions Welcome!!